In [1]} the authors proposed a detection-based solution, introducing a novel Layout Proposal Network (LPN) that counts and localizes vehicles in drone images, leveraging the spatial layout information (e.g., cars often park regularly). Furthermore, they also presented the CARPK and the PUCPR+ datasets, two collections of images suitable for the vehicle counting task that we exploited to evaluate the performance of our solution. In [2]} the authors presented instead ResCeption, a network that combines residual learning with Inception-style layers [3]} and is able to count vehicles in one look. The authors of [4]} proposed an approach that claims to be able to count objects belonging to a generic class not decided a priori, i.e., a class-agnostic counting network. To this end, the network must be adapted using very few annotated data. They achieved this by recasting the counting problem as an image matching problem, where counting instances is performed by matching (self-similar patches) within the same image. This solution is tested by counting bacterial cells, people and vehicles. Aich et al. in [5]} proposed a simple and effective way to improve one-look regression models for object counting from images, enhancing them by regulating activation maps from the final convolution layer of the network with coarse ground-truth activation maps generated from simple dot annotations. They called this strategy Heatmap Regulation (HR). These mentioned solutions are not designed to work on embedded real-time devices due to time constraints and the limited available computing resources. One prior method able to perform onboard computations is described in [6]}, where the authors presented a detection-based solution computationally inexpensive for vehicle detection in uav imagery.
